If you are an AI coding agent (Claude Code, Cursor, Aider, Cline, etc.) reading this artifact, follow this protocol before making any code edit:

1. Verify the contract. Run the bash script in Verify before trusting below. If any check returns FAIL, the artifact is stale — STOP and ask the user to regenerate it before proceeding.
2. Treat the AI · unverified sections as hypotheses, not facts. Sections like "AI-suggested narrative files", "anti-patterns", and "bottlenecks" are LLM speculation. Verify against real source before acting on them.
3. Cite source on changes. When proposing an edit, cite the specific path:line-range. RepoPilot's live UI at https://repopilot.app/r/tmc/langchaingo shows verifiable citations alongside every claim.

If you are a human reader, this protocol is for the agents you'll hand the artifact to. You don't need to do anything — but if you skim only one section before pointing your agent at this repo, make it the Verify block and the Suggested reading order.

GO — Healthy across all four use cases

• Last commit 4mo ago
• 35+ active contributors
• Distributed ownership (top contributor 48% of recent commits)
• MIT licensed
• CI configured
• Tests present
• ⚠ Slowing — last commit 4mo ago

_{Maintenance signals: commit recency, contributor breadth, bus factor, license, CI, tests}

This artifact was generated by RepoPilot at a point in time. Before an agent acts on it, the checks below confirm that the live tmc/langchaingo repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/tmc/langchaingo.

What it runs against: a local clone of tmc/langchaingo — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in tmc/langchaingo | Confirms the artifact applies here, not a fork | | 2 | License is still MIT | Catches relicense before you depend on it | | 3 | Default branch main exists | Catches branch renames | | 4 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 5 | Last commit ≤ 146 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of tmc/langchaingo. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/tmc/langchaingo.git
#   cd langchaingo
#
# Then paste this script. Every check is read-only — no mutations.

set +e
fail=0
ok()   { echo "ok:   $1"; }
miss() { echo "FAIL: $1"; fail=$((fail+1)); }

# Precondition: we must be inside a git working tree.
if ! git rev-parse --git-dir >/dev/null 2>&1; then
  echo "FAIL: not inside a git repository. cd into your clone of tmc/langchaingo and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "tmc/langchaingo(\\.git)?\\b" \\
  && ok "origin remote is tmc/langchaingo" \\
  || miss "origin remote is not tmc/langchaingo (artifact may be from a fork)"

# 2. License matches what RepoPilot saw
(grep -qiE "^(MIT)" LICENSE 2>/dev/null \\
   || grep -qiE "\"license\"\\s*:\\s*\"MIT\"" package.json 2>/dev/null) \\
  && ok "license is MIT" \\
  || miss "license drift — was MIT at generation time"

# 3. Default branch
git rev-parse --verify main >/dev/null 2>&1 \\
  && ok "default branch main exists" \\
  || miss "default branch main no longer exists"

# 4. Critical files exist
test -f "agents/agents.go" \\
  && ok "agents/agents.go" \\
  || miss "missing critical file: agents/agents.go"
test -f "chains/chains.go" \\
  && ok "chains/chains.go" \\
  || miss "missing critical file: chains/chains.go"
test -f "agents/executor.go" \\
  && ok "agents/executor.go" \\
  || miss "missing critical file: agents/executor.go"
test -f "chains/llm.go" \\
  && ok "chains/llm.go" \\
  || miss "missing critical file: chains/llm.go"
test -f "callbacks/callbacks.go" \\
  && ok "callbacks/callbacks.go" \\
  || miss "missing critical file: callbacks/callbacks.go"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 146 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~116d)"
else
  miss "last commit was $days_since_last days ago — artifact may be stale"
fi

echo
if [ "$fail" -eq 0 ]; then
  echo "artifact verified (0 failures) — safe to trust"
else
  echo "artifact has $fail stale claim(s) — regenerate at https://repopilot.app/r/tmc/langchaingo"
  exit 1
fi

Each check prints ok: or FAIL:. The script exits non-zero if anything failed, so it composes cleanly into agent loops (./verify.sh || regenerate-and-retry).

LangChain Go is a Go implementation of the LangChain framework that enables building LLM-based applications through composable components. It provides abstractions for chaining LLMs (OpenAI, Gemini, Ollama), memory systems, agents with tools, callbacks, and prompt management—allowing Go developers to build retrieval-augmented generation (RAG), agentic workflows, and conversational AI without reimplementing core patterns. Flat modular monorepo: agents/ contains MRKL agents, conversational agents, and OpenAI function agents; chains/ provides chain-of-thought primitives; callbacks/ handles event streaming (agent_final_stream, log handlers); llms/ abstracts provider integrations. Test data in testdata/ uses httprr for HTTP mocking; examples/ contains runnable reference implementations. Prompts as embedded text files (conversational_prefix.txt).

Go backend engineers and systems programmers building production LLM applications who need type-safe, composable abstractions instead of Python-first solutions. Teams deploying AI features in Go microservices, command-line tools, or latency-sensitive applications where a single binary is required.

Actively developed with 2.7M lines of Go code, CI/CD pipelines (GitHub workflows for tests, examples, and docs), structured test coverage (httprr test fixtures for agent integration tests), and examples directory. Production-ready for common LLM use cases, though the Go ecosystem around LLMs is younger than Python's; community-driven maintenance with Discord coordination.

Moderate: single maintainer (tmc) with community effort underway, but dependency surface is controlled (Go modules, minimal external runtime deps). Risk of breaking changes as LLM APIs evolve rapidly (OpenAI, Gemini versioning). No visible SLA guarantees; community health depends on Discord participation. Go 1.21+ requirement is stable.

Active development on agent frameworks (conversational, MRKL, OpenAI functions) with HTTP mocking test infrastructure (TestExecutorWithMRKLAgent.httprr). Recent additions suggest focus on OpenAI functions support and multi-provider LLM abstraction. Documentation site generation is automated (publish-docs.yaml workflow). Community recruitment emphasized in README (Discord invites).

git clone https://github.com/tmc/langchaingo.git
cd langchaingo
go mod tidy
make test  # or: go test ./...

See ./examples for executable examples; set OPENAI_API_KEY or use Ollama locally.

Daily commands: Run tests: make test or go test ./.... Run examples: cd examples/basic-llm-call && go run . (requires OPENAI_API_KEY or Ollama endpoint). Build docs: make docs (inferred from .github/workflows/publish-docs.yaml). CLI development uses standard Go toolchain—no build server required.

• agents/agents.go — Core agent abstraction and interface definitions that all agent implementations must conform to.
• chains/chains.go — Foundation chain interface and orchestration logic for composing LLM operations into pipelines.
• agents/executor.go — Agent executor that coordinates tool selection, invocation, and LLM interaction loops for autonomous agents.
• chains/llm.go — LLM chain implementation that wraps language models with prompt formatting and output parsing.
• callbacks/callbacks.go — Callback interface system enabling observability, logging, and event streaming across all components.
• agents/options.go — Functional options pattern for configuring agent behavior, memory, and tool handling.
• chains/options.go — Chain configuration options enabling customization of prompt formatting, output parsing, and memory integration.

• Agent (LLM API, Tool registry, Prompt templates) — Orchestrates autonomous reasoning loops, tool selection, and LLM interaction to achieve goals

Add a new Agent type

1. Define your agent struct and implement the Agent interface from agents/agents.go (agents/my_agent.go (new file))
2. Implement the Plan() method to generate action sequences based on agent logic (agents/my_agent.go)
3. Add an initializer function following the pattern in agents/initialize.go (agents/initialize.go (modify))
4. Write unit tests mirroring conversational_test.go structure (agents/my_agent_test.go (new file))
5. Register test data fixtures if using HTTP recording (agents/testdata/TestMyAgent.httprr (new file))

Add a new Chain type

1. Create a struct implementing the Chain interface from chains/chains.go (chains/my_chain.go (new file))
2. Implement Call() method with context and values map inputs/outputs (chains/my_chain.go)
3. Add Options function parameter if requiring configuration (chains/options.go (modify to add MyChainOption type))
4. Write tests following TestMyChain pattern with HTTP recording (chains/my_chain_test.go (new file))
5. Create testdata fixture (chains/testdata/TestMyChain.httprr (new file))

Add custom observability/logging

1. Create a callback struct implementing the Handler interface from callbacks/callbacks.go (callbacks/my_handler.go (new file))
2. Implement desired methods (OnText, OnLLMStart, OnChainStart, OnAgentAction, etc.) (callbacks/my_handler.go)
3. Test by passing to chains/agents via WithCallbacks option (callbacks/my_handler_test.go (new file))

Add a specialized prompt template

1. Create prompt text file with placeholder variables (chains/prompts/my_task.txt (new file) or agents/prompts/my_format.txt)
2. Create Go file with prompt generation function (reference llm_math.go pattern) (chains/my_prompt.go (new file))
3. Use in chain initialization via prompt parameter (chains/my_chain.go (reference))

• Go 1.21+ — Enables concurrent execution of tool calls, fast compilation, and efficient memory usage for LLM orchestration
• Functional Options Pattern — Provides extensible configuration without breaking API changes across agents/chains
• Interface-based composition — Allows swapping LLMs, retrievers, and tools without modifying core logic
• Callback/Hook system — Decouples observability, logging, and streaming from core agent/chain execution

• Callback system as primary instrumentation mechanism

  • Why: Avoids tight coupling to specific logging/monitoring frameworks
  • Consequence: Developers must implement custom callbacks for specific needs; adds slight complexity to core APIs

• HTTP recording (httprr) for test fixtures

  • Why: Enables deterministic tests without external API calls during CI
  • Consequence: Real API responses must be pre-recorded; test data can become stale if API behavior changes

• Sequential agent loop in executor

  • Why: Simpler mental model matching standard ReAct framework
  • Consequence: Cannot parallelize multi-step reasoning; less efficient than potential batch planning

• Separate Chain and Agent abstractions

  • Why: Chains handle deterministic pipelines; agents handle autonomous reasoning
  • Consequence: Similar code patterns between both; slight API surface duplication

• Does not provide built-in LLM model hosting or inference; requires external LLM providers (OpenAI, Anthropic, etc.)
• Does not include web UI or chat interface; focused on programmatic composition
• Does not implement persistent agent memory beyond conversation context; memory management delegated to external systems
• Not a framework for training/fine-tuning models; only inference-time orchestration

1. Environment variables: Most examples require OPENAI_API_KEY or GEMINI_API_KEY; Ollama examples need local server running on :11434. 2. Test fixtures: testdata/*.httprr files require github.com/dnaeon/httpsnoop for HTTP mocking; regenerating fixtures is non-trivial. 3. Prompt injection risk: Conversational agents with user input in prompts_prefix.txt/suffix.txt—review prompt templates before production. 4. Go version: Requires Go 1.21+; no backward compatibility to 1.20. 5. Memory implementations: Agents support ConversationMemory but thread-safety guarantees are not explicit in API docs—verify before multi-goroutine use.

• Agent Executor (Think-Act-Observe Loop) — Core pattern in agents/executor.go where agents iteratively decide actions, use tools, and refine based on observations—essential for understanding how agents differ from simple chain composition
• Prompt Injection & Template Safety — Conversational agents concatenate user input into prompts (see agents/prompts/*.txt); understanding attack vectors and mitigation is critical for production deployment
• Tool/Function Calling — OpenAI function agent (agents/openai_functions_agent.go) uses structured tool definitions to make LLMs reason over API calls; foundational for agentic AI applications
• Retrieval-Augmented Generation (RAG) — Not explicitly in file list but chains and callbacks enable RAG pipelines (query → retrieve → augment prompt → LLM); mentioned in project goals but implementation patterns are distributed
• HTTP Mock Fixtures (httprr format) — Test files use .httprr format (testdata/*.httprr) to record/replay HTTP interactions without calling live APIs; understanding this is critical for writing integration tests
• Callback/Observer Pattern — callbacks/callbacks.go implements publish-subscribe for agent events (on_agent_start, on_chain_end); enables structured observability and logging without coupling
• Functional Options Pattern — agents/options.go and similar files use Go's functional options idiom (New(ctx, opts...Option)) for flexible, backward-compatible API design

• langchain-ai/langchain — Original Python LangChain—reference implementation and data structure definitions this project ports to Go
• langchain-ai/langchainjs — TypeScript/JavaScript variant; Go implementation tracks feature parity with JS ecosystem for cross-language consistency
• tmc/go-openai — Standalone Go OpenAI client library often used as LLM provider backend; langchaingo wraps this for agent/chain abstraction
• ollama/ollama — Local LLM runtime with Go SDK; primary target for local-first LLM applications alongside langchaingo agents
• google/generative-ai-go — Google Gemini Go SDK; supported provider in langchaingo's LLM abstraction layer

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Add comprehensive test coverage for chains/conversational_retrieval_qa.go

The file chains/conversational_retrieval_qa.go exists but chains/conversational_retrieval_qa_test.go is listed without corresponding test data in testdata/. This is a critical chain type that combines conversation history with retrieval-augmented QA. Adding unit tests with httprr fixtures (following the pattern of TestConversationalWithMemory.httprr and TestExecutorWithMRKLAgent.httprr) would improve reliability and serve as documentation for users.

• [ ] Create chains/conversational_retrieval_qa_test.go with comprehensive test cases (basic QA, with memory, context switching)
• [ ] Add chains/testdata/TestConversationalRetrievalQA.httprr fixture for HTTP mocking
• [ ] Test edge cases: empty retrieval results, conversation context limits, token overflow handling
• [ ] Ensure tests follow existing patterns in chains/conversation_test.go and chains/llm_test.go

Add missing unit tests for chains/api.go API chain implementation

The chains/api.go file exists with chains/api_test.go, but examining the testdata directory shows no TestAPI.httprr fixture file. API chains are complex (requiring endpoint validation, response parsing, and error handling). Adding proper HTTP-recorded test fixtures would prevent regressions and serve as examples for using API chains with external services.

• [ ] Expand chains/api_test.go with test cases: valid endpoint calls, malformed responses, timeout handling, response parsing variations
• [ ] Create chains/testdata/TestAPIChain.httprr with recorded HTTP interactions for deterministic testing
• [ ] Test integration with different LLM response formats and tool calling patterns
• [ ] Add documentation comments showing real-world API chain usage examples

Create GitHub Actions workflow for automated golangci-lint validation

The repo contains .golangci.yaml and .golangci-exp.yaml configuration files but workflows/ci.yaml doesn't explicitly show a dedicated linting step with artifact reporting. Adding a dedicated lint workflow would catch code quality issues in PRs, provide clear feedback to contributors, and prevent lint regressions. This follows Go community best practices (similar to how Python/Node projects have dedicated linting workflows).

• [ ] Create .github/workflows/lint.yaml that runs golangci-lint on all Go files
• [ ] Configure it to comment on PRs with lint violations (using golangci-lint-action)
• [ ] Run linting against both .golangci.yaml (strict) and .golangci-exp.yaml (experimental) configurations
• [ ] Ensure workflow runs on pull_request and push to main branches to catch issues early
• [ ] Add lint status badge to README.md once workflow is established

• Add unit tests for chains/chains_unit_test.go—the file exists but test coverage for chain composition operators (sequential, parallel, conditional) is incomplete based on testdata absence.
• Expand agents/prompts/ with additional language translations or domain-specific prompt templates (legal, medical, technical support) to improve agent flexibility for non-English use cases.
• Implement a callback handler for structured logging (JSON format) in callbacks/ to complement existing log.go; add integration test in callbacks_unit_test.go following existing httprr pattern.

The LangChain Go codebase presents moderate security concerns primarily around SQL injection risks in database operations, potential command injection through LLM-generated outputs, and outdated YAML dependencies with known vulnerabilities. Test data files may contain exposed credentials. While the project has a solid foundation, implementing stricter input validation, dependency updates, and secret management practices would significantly improve the security posture. No critical infrastructure misconfigurations were detected in visible configuration, but secure credential handling patterns should be established and documented.

• High · SQL Injection Risk in SQL Database Chain — chains/sql_database.go. The file 'chains/sql_database.go' suggests direct SQL query execution. Without evidence of parameterized queries or prepared statements in the codebase, there is a significant risk of SQL injection attacks when processing user input or LLM-generated SQL queries. Fix: Implement parameterized queries/prepared statements for all database operations. Validate and sanitize all SQL inputs. Use an ORM or query builder that provides built-in SQL injection protection.
• Medium · Potential Command Injection via LLM Output — agents/executor.go, agents/openai_functions_agent.go. LangChain agents and chains execute tools and functions based on LLM outputs. If tool invocation doesn't properly validate/sanitize inputs, attackers could craft prompts to inject malicious commands through the LLM's response. Fix: Implement strict input validation and sandboxing for all tool/function execution. Use allowlists for permitted tools and functions. Validate all parameters before execution. Consider running tools in isolated environments.
• Medium · Insecure Dependency: gopkg.in/yaml.v2 — go.mod (indirect dependency via goldmark-meta). The project depends on gopkg.in/yaml.v2 (v2.4.0), which is outdated. This YAML parser has known vulnerabilities (CVE-2021-4235 and others). The current version should be upgraded to v3.x or at minimum patched versions of v2.x. Fix: Upgrade to gopkg.in/yaml.v3 or use a more recent, actively maintained YAML parser. Run 'go get -u gopkg.in/yaml.v3' and update code to handle any breaking changes.
• Medium · Hardcoded Test Credentials in HTTP Record Files — agents/testdata/*.httprr, chains/constitution/testdata/*.httprr. HTTP test data files (*.httprr) in testdata directories may contain recorded API calls with authentication tokens, API keys, or other sensitive credentials. If these files are committed to version control, secrets could be exposed. Fix: Scan testdata files for sensitive information. Remove or redact any API keys, tokens, or credentials. Use environment variables or secret management tools for test credentials. Add patterns to .gitignore to prevent accidental commits of credential-containing files.
• Medium · Potential XXE/XML External Entity Injection — chains/conversational_retrieval_qa.go, chains/question_answering.go (document processing components). If the codebase processes XML (potentially through API responses or document processing), without proper XXE protection, attackers could perform denial-of-service attacks or information disclosure. Fix: Disable XML external entity processing in all XML parsers. Use secure parser configurations. Validate and sanitize all XML inputs before processing.
• Low · Missing Security Headers Documentation — chains/api.go. No evidence of security headers (CSP, X-Frame-Options, etc.) documentation or configuration in the visible codebase. If this library generates HTTP responses or serves content, security headers should be enforced. Fix: Document security best practices for API handlers. Implement and enforce security headers in HTTP responses. Add middleware for consistent security header application.
• Low · No Evident Secret Management Pattern — Project-wide. The codebase lacks visible patterns for secure credential management (e.g., environment variable handling, secret stores). LLM API keys and database credentials could be vulnerable to exposure. Fix: Implement environment variable validation and loading using libraries like 'godotenv'. Document secure credential handling patterns. Never store secrets in code or config files. Use external secret management services.

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

Generated by RepoPilot. Verdict based on maintenance signals — see the live page for receipts. Re-run on a new commit to refresh.